Discrete survival endpoints can occur in observational studies where there is periodic follow-up or when time is measured discretely. Examples where these data commonly arise include situations where subclinical diagnosis of disease is made through routine checkups and surveillance. These include the systematic use of colonoscopy for detecting polyp growth in colorectal cancer and the use of regular mammographies for the detection of tumor mass in the case of breast cancer. In such settings, the exact event times are interval censored and unobserved. When the survival endpoint is acknowledged to be discrete or interval censored, common semiparametric methods for the analysis of the observed failure times include the discrete-time proportional hazards model and the proportional odds model. Inference using semiparametric survival models is dependent on the observed censoring distribution when the semiparametric assumption fails to hold. This renders the interpretation of the results scientifically unmeaningful since the target population i ill-defined. Recently, some authors have proposed the use of weighted estimators to remove the effect of censoring on the estimand of interest when semiparametric assumptions fail to hold. While these proposed estimators provide consistent and reproducible results under model misspecification, they have only been developed for settings where survival times are measured continuously and only in the case of K-sample comparisons. The goal of the research proposed here is to develop a class of censoring robust discrete survival estimators and establish their asymptotic behavior. In addition, we will extend the previous work censoring robust estimators for continuous survival data by developing these approaches for general regression strategies that may involve multiple adjustment covariates.